Difference between revisions of "Classification of Evoked Local-Field Potentials in Rat Barrel Cortex using Hyper-dimensional Computing"
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Revision as of 09:29, 16 September 2021
One of the most ambitious goals of neuroscience and its neuroprosthetic applications is to interface intelligent electronic devices with the biological brain to cure neurological diseases. Neural coding is the branch of neuroscience that investigates the relationship between stimulus and neuronal responses. This emerging research field builds on our growing understanding of brain circuits and on recent technological advances in miniaturization of implantable multielectrode-arrays (MEAs) to record brain signals at high spatio-temporal resolution. Data processing is needed to decode useful information from the recorded neural activity to better understand the function of underlying neural circuits and, in perspective, to operate neuroprosthetic devices. In this context, artificial intelligence combined with low-power embedded devices is a very promising starting point towards real-time decoding of cerebral activities with low power consumption digital processors for brain-machine interfacing and neuroprosthetic applications .
Brain-inspired hyperdimensional computing (HDC) explores the emulation of cognition by computing with hypervectors as an alternative to computing with numbers. HDC has proven to be promising for energy-efficient computing applied to biosignal classification .
This project focuses on processing data of evoked Local Field Potentials (LFPs) recorded from the rat barrel cortex using a miniaturized 16-by-16 MEA while stimulating the principal whisker. The sensor has been implanted in vivo and 2D images have been acquired from different cortical depths. The deflection of the whisker is performed by means of a piezo-electric bender using various stimulation amplitudes. The aim of the project is to assess the performance of HDC in classifying different external stimulus applied to the animal.
The task includes the following main sub-points:
- Understand the LFP basics and interpret the dataset.
- Develop (high-level Phython or Matlab) machine learning or deep learning algorithm to classify the stimulation amplitudes or to detect signal onset.
- Map the algorithm in the hardware (C-programming PULP, parallel computing).
- Conduct in-vivo experiments to validate the method with a realistic setting.
The task is anyways flexible and it will be adapted to the student's skills and will.
- Semester project
- 20% Theory
- 80% Programming
- Knowledge in Machine Learning (preprocessing, feature extraction, classifier, supervised-learning)
- Embedded system programming
- Python, C/C++, Matlab
-  X. Wang, et al., Embedded Classification of Local Field Potentials Recorded from Rat Barrel Cortex with Implanted Multi-Electrode Array, 2018
-  A. Rahimi, et al., Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition, 2016